1. Field of the Invention
This invention relates to analog neural networks and DNA.
2. Description of Related Art
An analog neural network (ANN) is a parallel, distributed, information-processing system that responds to input information. An ANN can be “trained” by exposure to training data so that it is able to detect or recognize a pattern in input data. An ANN consists of many processing units that send signals to other processing units in the network. The signals that a particular processing unit receives are weighted to determine the signal that the unit then sends to other processing units to which it is connected. In most neural network models, a saturating function is applied to signal sent by one processing unit to another so that outputs of the unit have a value between zero and one. Processing units that operate in this manner are known as “perceptrons.”
Neural networks model tasks such as pattern classification, clustering of data, non-linear input/output mapping, associative memory storage, vision and speech preprocessing, and the solution of combinatorial optimization problems.
The parallel operations and interactions of the processing units of a neural network may give rise to collective properties that include production of a content-addressable or an associative memory.
Single-layer perceptrons (SLPs) and multi-layer perceptrons (MLPs) are “feedforward” neural network models that include one or more layers of processing units, i.e., perceptrons. These models propagate the input signal through the network one layer at a time. An SLP consists of a single perceptron layer, and can classify an input vector into one of two classes. An MLP consists of an “input layer” of sensory units, one or more “hidden layers” of perceptrons, and one “output layer” of processing units. In an MLP, every unit in a layer is connected to every unit in the layer “below.” An MLP maps a set of variables in an input vector from a multidimensional input space onto a multidimensional output space of a set of output variables. The structure of the network and the values of the weights determine the result of the input/output mapping. The values of the weights that enable the network to accomplish the mapping are chosen through a training process that identifies those weights that best approximate the desired mapping for every pairing of input data and output data in the training set. The neural network adapts to its function according to the training information presented during training.
Feed-forward MLPs trained by back-propagation of errors are used successfully for non-linear signal processing and speech recognition.
In medicine, such MLPs use medical data relating to diseases to assist in diagnosis and prognosis. In analyzing data related to myocardial infarction, tumor classification, and thyroid function, for example, diagnoses based on analysis of data by a neural network have been more accurate than those based on analyses carried out by other paradigms. ANNs have also been trained to prognostically predict the future re-occurrence of breast cancer in patients, and to analyze the risk of developing diabetes mellitus.
Neural network algorithms have been successfully applied to analyze relationships between structural or physicochemical properties of molecules and their biological or biochemical activities.
ANNs have also been trained to identify sets of genetic marker loci involved in disease etiology; and to identify nucleotide sequences that encode a protein structural motif.
Microarray and cDNA grid hybridization techniques have been developed that simultaneously detect and quantitate the expression of many different genes in a sample of cells in a single experiment. This procedure has come to be referred to as “gene profiling” or “expression profiling.” A number of different and effective methods for gene expression profiling have been developed. These methods typically involve isolating cellular RNA, and preparing a set of cDNA or amplified RNA molecules that represent the mRNA molecules present in the cells of interest, labeling these with a detectable label such as a fluorochrome or a radioisotope, hybridizing the labeled polynucleotides to a DNA or oligonucleotide microarray or grid, and identifying sites in the microarray or grid at which the labeled polynucleotides hybridized.
Neural network training algorithms have also been used to organize and analyze gene expression data for hundreds of different genes that have been collected in gene expression profiling experiments.
The above-discussed ANNs are implemented using computers and software that carry out the mathematical functions underlying the operation of the ANNs.